

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCamelCase      					=     {
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCamelCase      					=     [
					    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GPTNeoForCausalLM""",
					    """GPTNeoForQuestionAnswering""",
					    """GPTNeoForSequenceClassification""",
					    """GPTNeoForTokenClassification""",
					    """GPTNeoModel""",
					    """GPTNeoPreTrainedModel""",
					    """load_tf_weights_in_gpt_neo""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCamelCase      					=     [
					    """FlaxGPTNeoForCausalLM""",
					    """FlaxGPTNeoModel""",
					    """FlaxGPTNeoPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neo import (
										    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoForCausalLM,
										    GPTNeoForQuestionAnswering,
										    GPTNeoForSequenceClassification,
										    GPTNeoForTokenClassification,
										    GPTNeoModel,
										    GPTNeoPreTrainedModel,
										    load_tf_weights_in_gpt_neo,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
					import sys

					_lowerCamelCase      					=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






"""simple docstring"""






from itertools import count




def __magic_name__	(				_lowerCamelCase:		int = 50			)					->						int:

	'''simple docstring'''




	lowerCAmelCase												=  [1] * min_block_length

	for n in count(_lowerCamelCase			):
		fill_count_functions.append(1			)

		for block_length in range(_lowerCamelCase,					n + 1			):
			for block_start in range(n - block_length			):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1_000_000:
			break

	return n


if __name__ == "__main__":
					print(f"""{solution() = }""")


